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Generation of Human 3D Lung Tissue Cultures 3D-LTCs for Disease Modeling

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Digital twins for chronic lung diseases.

Apolline Gonsard¹, Martin Genet^2,3, David Drummond^4,5

¹Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, Paris, France.

European Respiratory Review : an Official Journal of the European Respiratory Society

|December 18, 2024

Summary

Digital twins offer real-time respiratory health visualization and personalized medicine. This review explores their potential in respiratory medicine for improved patient outcomes and disease understanding.

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Area of Science:

Digital health
Cyber-physical systems
Computational modeling

Background:

Digital twins integrate cyber-physical systems, modeling, and computation for bidirectional data flow.
Advancements in AI and connected devices enable real-time patient respiratory health visualization.
Sophisticated respiratory system modeling supports predictive therapeutic efficacy assessments.

Purpose of the Study:

To review the concept of digital twins in healthcare.
To explore the feasibility and potential benefits of digital twins in respiratory medicine.
To identify challenges associated with implementing digital twins in clinical practice.

Main Methods:

Review of current literature on digital twins and respiratory medicine.
Analysis of technological advancements enabling digital twin creation.
Discussion of potential applications and implementation hurdles.

Main Results:

Digital twins can provide real-time visualization of respiratory health.
They hold potential for predicting therapeutic responses and facilitating personalized medicine.
Researchers can utilize digital twins to study gene-environment-time interactions in chronic respiratory diseases.

Conclusions:

Digital twins present a promising avenue for personalized and timely respiratory care.
Their implementation could enhance disease understanding, early detection, and therapeutic adaptation.
Addressing implementation challenges is crucial for realizing the full potential of digital twins in respiratory medicine.